Industrial Technology Commons^™

Increasing The Prediction Accuracy Of Plant Oil Production Processes Through Adjusting The Parameters Of Machine Learning Models, Umidjon Ruziev, M.K. Shodiev, A.T. Rajabov

Chemical Technology, Control and Management

Vegetable oil production is characterized by high variability in output indicators due to nonlinear interactions between raw material parameters, equipment modes, and heat and mass transfer conditions. Existing approaches to applying machine learning in this field, as a rule, do not account for the impact of hyperparameter adjustments on forecasting quality across specific technological stages. The article presents a systematic methodology for adjusting model parameters (Ridge regression, SVR, GBM, LSTM) applied to three key tasks: predicting residual oil content in oil cake, color index during bleaching, and free fatty acid content during deodorization. In a set of 1000 observations, including …

Improving Self-Diagnostic Methods Of Flow Measurement Systems Based On Artificial Intelligence, Elbek Ortikov

Chemical Technology, Control and Management

The article examines methods for improving self-diagnostics of consumption measurement systems based on artificial intelligence in the context of industry digitalization and the development of cyber-physical systems. It has been shown that traditional flow meters used to measure the flow rate of liquids and gases are subject to mechanical, hydraulic, electronic, and hidden failures, which reduce the accuracy and reliability of measurements. A justification for the need to transition from classical maintenance methods to intelligent self-control methods that ensure the detection of anomalies and hidden malfunctions in real time is presented. A multi-level architecture of intelligent self-diagnosis is proposed, including …

Empirical Analysis Of Machine Learning Models For Predicting Equipment Failures Using Iot Sensor Data, Yusuf Shodiyevich Avazov

Chemical Technology, Control and Management

This article examines the problem of detecting and predicting industrial equipment faults using IoT sensor data through machine learning techniques. Sensor readings such as temperature, vibration, pressure, voltage, and current, as well as FFT-based features, were statistically analyzed. Class imbalance and low signal informativeness were identified as key factors limiting model accuracy. Results obtained from Logistic Regression, Random Forest, and XGBoost models were comparatively evaluated, showing that when ROC-AUC values remain around 0.5, distinguishing fault and non-fault states becomes challenging. Correlation and feature-importance analyses confirmed the absence of strong dominant indicators. The findings highlight the need to improve sensor architecture …

Algorithms For Fast Fire Risk Prediction And Real-Time Data Processing, Mirzoyan Mirzaaxmedovich Kamilov, Tolaniddin Ramziddinovich Nurmukhamedov, Oybek Zokirovich Koraboshev, Bakhodir Saydullayevich Achilov

Chemical Technology, Control and Management

Ensuring fire safety in facilities with high fire risk is one of the pressing problems of modern society. Nowadays, there is a great need for accurate and effective prediction systems for fire prevention and rapid response. Since traditional methods do not provide the ability to quickly analyze and predict in real time, the development of algorithms and modern approaches using modern technologies is of great importance. This article analyzes fire risk prediction algorithms, their principles of operation and effectiveness, and considers methods for assessing and predicting fire risk using Artificial Intelligence (AI), Machine Learning (ML), and Big Data technologies. The …

Synchronization Of The Training Of Specialists In The Automation Of Technological Processes In Accordance With The Dynamics Of The Taxonomy Of Learning Goals, Yusuf Shodievich Avazov, Kamola Abdullaeva

Chemical Technology, Control and Management

Changes in the characteristics of modern ones, reflecting them as the "digital generation", inevitably necessitate electronic interactive, mobile and mixed, machine learning, especially in the field of automation and control of technological processes and production. As a consequence of reflecting these realities, there have been parallel changes in the taxonomy of learning objectives - from the classical to the revised rethought and digital.

In order to study the problem of synchronizing the training of future specialists in the field of automation and control of technological processes and production and funny knowledge in accordance with the dynamics of the taxonomy of …

Using Reinforcement Learning To Improve Network Reliability Through Optimal Resource Allocation, Henley Wells

Graduate Theses and Dissertations

Networks provide a variety of critical services to society (e.g. power grid, telecommunication, water, transportation) but are prone to disruption. With this motivation, we study a sequential decision problem in which an initial network is improved over time (e.g., by adding or increasing the reliability of edges) and rewards are gained over time as a function of the network’s all-terminal reliability. The actions during each time period are limited due to availability of resources such as time, money, or labor. To solve this problem, we utilized a Deep Reinforcement Learning (DRL) approach implemented within OpenAI-Gym using Stable Baselines. A Proximal …

Advancing Statistical Learning And Decision Modeling Using Irregularly-Sampled Multivariate Data For Managing Respiratory Diseases, Maryam Alimohammadi

Graduate Theses and Dissertations

Complex healthcare systems require efficient and effective data-driven decision making in various aspects. As patient data becomes more available, advanced statistical learning and machine learning techniques are applied to improve data-driven decision making. However, patient health data, including clinical trial data, medical records, and electronic health records, are associated with several challenges. Patient health data includes medical information of a patient that may includedemographics, information relating to their health or illness, medications and treatments, etc. They are a combination of static and time series variables, with many censoring and missingness in the data, and are irregularly sampled in most cases. …

Predicting The Likelihood And Scale Of Wildfires In California Using Meteorological And Vegetation Data, Matthew Walters

Graduate Theses and Dissertations

Wildfires have devastating ecological, environmental, economical, and public health impacts through the deterioration of water and air quality, CO2 emissions, property damage, and lung illnesses. The early detection and prevention of wildfires allow for the minimization of these risks. The use of Artificial Intelligence (AI) in wildfire detection and prediction has been highly researched as a tool to assist firefighters in stopping wildfires in its early stages. The three common wildfire prediction categories include image and video detection, behavior prediction, and susceptibility prediction. Data such as climate, weather, vegetation, satellite images, and historical wildfire data is most commonly used. Many …

Sports Analytics: Putting The Fun Back Into Analytics, Walt Degrange

Operations Management Presentations

With the recent success of sports teams heavily using analytics (Dodgers, Patriots, Capitals, Warriors, Leicester City F.C.), does this mean that analytics has gained a foothold in the sports world? I use a k-means clustering model to determine if performance since 2015 in the four major US sports can support this question. And is there a career path that a high school student can use to become a sports analytics professional? This presentation attempts to answer that question by exploring all the areas of the application of analytics in sports. The final point the brief makes is that by using …